
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		import tensorflow as tf

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase    :   List[Any]						=			logging.get_logger(__name__)




@add_end_docstrings(__lowercase)
class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Any	,  *a						:List[str]	,  **a						:Optional[Any]		)						->					List[str]:
								super().__init__(*a	,  **a		)
								requires_backends(self	,  "vision"		)
								self.check_model_type(
								    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == "tf"
								    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:Union[str, Any]=None		)						->					Dict:
								__UpperCamelCase      :  Any       =   {}
								if top_k is not None:
															__UpperCamelCase      :  Dict       =   top_k
								return {}, {}, postprocess_params







	def __call__(   self						:str	,  a						:Union[str, List[str], "Image.Image", List["Image.Image"]]	,  **a						:Any		)						->					Any:
								return super().__call__(a	,  **a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								__UpperCamelCase      :  List[str]       =   load_image(a		)
								__UpperCamelCase      :  Optional[Any]       =   self.image_processor(images=a	,  return_tensors=self.framework		)
								return model_inputs







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:Optional[Any]		)						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   self.model(**a		)
								return model_outputs







	def   _lowerCamelCase							(   self						:Any	,  a						:List[str]	,  a						:Tuple=5		)						->					Optional[Any]:
								if top_k > self.model.config.num_labels:
															__UpperCamelCase      :  str       =   self.model.config.num_labels

								if self.framework == "pt":
															__UpperCamelCase      :  Optional[Any]       =   model_outputs.logits.softmax(-1		)[0]
															__UpperCamelCase  ,		__UpperCamelCase      :  int       =   probs.topk(a		)
								elif self.framework == "tf":
															__UpperCamelCase      :  Optional[Any]       =   stable_softmax(model_outputs.logits	,  axis=-1		)[0]
															__UpperCamelCase      :  List[Any]       =   tf.math.top_k(a	,  k=a		)
															__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   topk.values.numpy(), topk.indices.numpy()
								else:
															raise ValueError(f'Unsupported framework: {self.framework}'		)

								__UpperCamelCase      :  Any       =   scores.tolist()
								__UpperCamelCase      :  Optional[int]       =   ids.tolist()
								return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a	,  a		)]

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)